Gastos Casa
08 enero, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 27/11/2023 | Comida | 69098 | Tami | Supermercado |
| 28/11/2023 | Diosi | 52000 | Tami | Veterinaria (control + vacuna) |
| 1/12/2023 | Electricidad | 93256 | Andrés | NA |
| 1/12/2023 | Diosi | 168000 | Tami | Consulta vet + hospitalización + exámenes |
| 2/12/2023 | Comida | 83183 | Tami | Supermercado |
| 2/12/2023 | Farmacia | 36819 | Tami | Diolasa + Clotrimazol + Mulcatel + Propolgea |
| 7/12/2023 | Diosi | 30000 | Tami | Consulta Veterinaria |
| 8/12/2023 | Agua | 15080 | Andrés | NA |
| 9/12/2023 | Comida | 57905 | Tami | Supermercado |
| 9/12/2023 | Comida | 20614 | Tami | Fork pedido el 28/11 |
| 17/12/2023 | Comida | 40000 | Andrés | NA |
| 17/12/2023 | Comida | 8000 | Andrés | almuerzo |
| 17/12/2023 | VTR | 22000 | Andrés | NA |
| 17/12/2023 | Comida | 42000 | Andrés | piwen |
| 17/12/2023 | Comida | 48432 | Tami | Supermercado |
| 17/12/2023 | Enceres | 16400 | Tami | Incoludido |
| 19/12/2023 | Aporte Basureros | 10000 | Tami | NA |
| 22/12/2023 | Netflix | 8326 | Tami | NA |
| 22/12/2023 | Diosi | 14700 | Andrés | Antiparasitario |
| 24/12/2023 | Comida | 79633 | Tami | Supermercado |
| 24/12/2023 | Comida | 19950 | Andrés | Empanadas |
| 25/12/2023 | Uber | 5595 | Tami | NA |
| 27/12/2023 | Diosi | 22970 | Andrés | arena |
| 30/12/2023 | Comida | 50000 | Andrés | jumbo la litad de los 97 |
| 2/1/2024 | Electricidad | 55466 | Andrés | pac enel |
| 2/1/2024 | Comida | 44542 | Tami | Supermercado |
| 5/1/2024 | Comida | 9516 | Tami | Burger King pre Maite |
| 5/1/2024 | Comida | 19590 | Tami | Barritas Soul |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.5173e+08 2 7.9775 4e-04 ***
## lag_depvar 9.6974e+10 1 1816.5433 <2e-16 ***
## Residuals 3.4913e+10 654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 894.621 13563.06 0.020548
## 2-0 29636.316 23885.088 35387.54 0.000000
## 2-1 22407.478 19020.399 25794.56 0.000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
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## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
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## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 31 25617.86 0 28331.57
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## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 502 51870.58 15505.303
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1927.03561 4001.01310 -498.96770 2471.97139 -2892.30551 546.85199
## 8 9 10 11 12 13
## -5614.52427 -1234.35473 -4017.53116 -518.29935 -5025.71992 -1755.63545
## 14 15 16 17 18 19
## -1045.01947 244.47846 -3344.58778 -510.53357 -2243.65074 6479.06857
## 20 21 22 23 24 25
## -1523.98240 -1219.97165 1454.34407 -1173.08683 235.74856 1708.13280
## 26 27 28 29 30 31
## -7054.89882 883.01144 8159.76050 530.04003 98.99825 -2293.54022
## 32 33 34 35 36 37
## 1639.64711 4661.83486 1287.09065 2558.10346 -1675.32593 4754.80465
## 38 39 40 41 42 43
## 4390.38526 -2129.73554 -2889.74497 -1077.25017 -10729.84373 7127.41179
## 44 45 46 47 48 49
## 2533.67730 1388.07487 8146.40798 853.28919 6686.57383 6958.05232
## 50 51 52 53 54 55
## -5560.67074 -4608.23611 -4972.54481 -7933.03984 5999.09161 -4091.54834
## 56 57 58 59 60 61
## -4972.25403 3709.67397 822.82620 -73.96413 105.82063 -5025.27374
## 62 63 64 65 66 67
## 18021.90224 3841.26642 -3411.51886 6072.47738 7568.69394 14954.15202
## 68 69 70 71 72 73
## 2205.03312 -12737.34380 -1102.32313 4801.46395 -4686.76392 -4295.89858
## 74 75 76 77 78 79
## -10472.46000 2320.97102 -5486.65519 902.01170 -6989.64291 329.72973
## 80 81 82 83 84 85
## -2534.71043 -2888.21112 -4144.07795 -785.18873 2090.41590 3604.69913
## 86 87 88 89 90 91
## 399.94662 -543.46461 138.02751 4254.58056 -1134.67415 1157.64272
## 92 93 94 95 96 97
## -2039.36721 -1054.78085 152.37046 256.30775 -7495.06853 2262.85114
## 98 99 100 101 102 103
## -8675.98689 -3141.83409 -4261.49494 -1994.48323 -1513.39434 2941.46170
## 104 105 106 107 108 109
## -2499.49386 2419.72133 -1268.35374 856.59438 2503.91033 -3184.86831
## 110 111 112 113 114 115
## -4799.48379 -991.94723 1766.89399 11605.44565 -1131.42775 2746.49412
## 116 117 118 119 120 121
## 4374.50946 3669.37742 -897.32572 -4556.09952 -3658.85657 2317.91723
## 122 123 124 125 126 127
## -1696.26564 1345.26658 8884.77766 1013.08653 289.87624 -2379.05591
## 128 129 130 131 132 133
## 2739.72726 7169.86501 1228.83078 -8293.29422 1793.83691 4203.32849
## 134 135 136 137 138 139
## -3037.63616 -1359.30030 -823.15198 -3865.97869 1133.90414 -518.67190
## 140 141 142 143 144 145
## -2941.03556 1648.27380 -1913.84633 -7887.31910 1864.05205 -3599.55230
## 146 147 148 149 150 151
## 1942.47165 -362.68582 927.65755 -425.55621 1289.23021 1153.97023
## 152 153 154 155 156 157
## 3347.74189 -4815.02690 -1210.80542 -3285.65906 5862.03098 9760.06574
## 158 159 160 161 162 163
## -3621.79398 -4998.64515 3335.63178 18.54468 2545.72800 -5998.70664
## 164 165 166 167 168 169
## -6918.60454 3899.79627 17234.21162 3759.05074 -228.51154 -2304.99491
## 170 171 172 173 174 175
## -1020.51087 3645.01652 -125.83995 -7991.01028 2809.65713 4321.48853
## 176 177 178 179 180 181
## 687.95999 8812.99498 -9059.23239 -3452.53323 -10783.79093 -11443.78318
## 182 183 184 185 186 187
## 879.89914 9000.78533 -1542.72016 5805.71487 6537.01325 13239.48261
## 188 189 190 191 192 193
## 8698.89285 -3706.30290 2706.42972 10610.06505 -1281.69134 -2161.45609
## 194 195 196 197 198 199
## -10078.08911 -6350.27389 1142.07278 -5299.32673 -9938.25674 5105.90819
## 200 201 202 203 204 205
## -3231.80894 -1905.68763 -1002.62935 6304.96278 9809.50866 657.02598
## 206 207 208 209 210 211
## 2995.15154 3196.96283 5910.96699 13024.91346 -5333.43656 -11079.69911
## 212 213 214 215 216 217
## -5653.75217 -10666.76364 -5308.48374 1243.06788 -13238.43343 15990.92121
## 218 219 220 221 222 223
## 7704.62081 1548.64209 26723.19258 12949.14386 7882.81441 14605.81169
## 224 225 226 227 228 229
## -3206.97534 -1191.66253 4222.80915 798.45906 3128.05003 9373.96380
## 230 231 232 233 234 235
## 6284.32689 -1425.13116 -1441.07823 9731.26841 -11104.70623 -7109.96892
## 236 237 238 239 240 241
## -8504.76935 -10203.40552 2831.89791 1180.08460 -8430.86320 -9235.13745
## 242 243 244 245 246 247
## 8741.56530 -7940.66172 2213.69112 -10506.54876 -4391.75887 1063.26060
## 248 249 250 251 252 253
## 706.65251 -12563.88371 3238.23139 1759.13227 3973.03052 1983.91903
## 254 255 256 257 258 259
## -1266.09170 11023.86786 20938.64786 3546.17206 -3929.65309 4334.99046
## 260 261 262 263 264 265
## -1446.18544 3920.10430 -4645.59675 -10793.74654 -4809.91284 -669.40129
## 266 267 268 269 270 271
## -5331.73672 8569.41119 -4341.78532 4064.58897 -2163.15078 4342.21411
## 272 273 274 275 276 277
## 687.26657 7284.88878 -1331.85969 12063.97719 -4388.48160 1817.28833
## 278 279 280 281 282 283
## -278.80265 7916.27130 -4899.60474 -2676.89808 -11262.61205 -2839.14285
## 284 285 286 287 288 289
## 18461.26390 7871.08214 2909.70724 -448.48745 1042.50643 6518.63718
## 290 291 292 293 294 295
## 7066.16633 -18527.07551 -11197.69181 -8334.43730 9361.37218 2937.62715
## 296 297 298 299 300 301
## -1259.86500 27308.30772 10355.36490 5281.75024 9906.16436 3314.96465
## 302 303 304 305 306 307
## -605.17887 8243.27977 -23894.86524 -3527.53196 -222.82246 -7016.95774
## 308 309 310 311 312 313
## -4113.82492 2751.13613 -9312.78128 -3462.48601 -8433.25381 1239.17716
## 314 315 316 317 318 319
## -3414.09606 1775.76252 -4293.72838 27203.83431 -587.64861 3390.73219
## 320 321 322 323 324 325
## 10952.20866 5831.51456 32657.04980 5778.82844 -20296.15913 2060.64215
## 326 327 328 329 330 331
## 1360.63579 -6239.52072 -1630.99435 -33206.37825 523.37942 -2594.39496
## 332 333 334 335 336 337
## -370.26450 -3402.84370 3846.12379 -585.38204 -7081.83499 -3314.57462
## 338 339 340 341 342 343
## -2398.18899 -7881.13793 3582.60132 -1548.56242 -1904.12482 -1155.33253
## 344 345 346 347 348 349
## 29.11442 361.16710 -1712.19410 -9544.91209 -13414.45218 1965.60776
## 350 351 352 353 354 355
## -4586.36754 -3937.23599 -6263.26543 1433.38029 1136.72421 2559.96267
## 356 357 358 359 360 361
## -3896.17006 -675.16375 535.31068 6901.18951 269.78937 -39.99740
## 362 363 364 365 366 367
## 2581.69319 -2717.84697 -882.57422 -8756.57985 -4743.59676 -6367.42241
## 368 369 370 371 372 373
## -5156.28451 -7487.94003 4725.93730 187.63534 6968.97798 -7675.16524
## 374 375 376 377 378 379
## -2388.51076 -3520.08972 -2618.66235 -12614.16049 1616.98834 -10852.24188
## 380 381 382 383 384 385
## 5379.77318 9137.69416 3072.75677 -2404.88834 1570.56703 6735.40936
## 386 387 388 389 390 391
## 11486.12609 -5599.52358 -5265.95072 -144.59486 8569.66297 1922.43740
## 392 393 394 395 396 397
## 11332.07851 -9651.55067 2839.85473 797.67838 638.77268 -586.54714
## 398 399 400 401 402 403
## -518.47100 -14460.67554 8370.30836 -1204.68925 -1406.57709 6936.53944
## 404 405 406 407 408 409
## -7886.38536 -1352.00122 -2591.15507 -5896.59545 -2990.40147 -4058.63570
## 410 411 412 413 414 415
## -8918.29379 5893.57809 1512.81135 -7459.73798 -7851.01979 13996.84595
## 416 417 418 419 420 421
## 3794.88457 4517.58798 -7961.95350 -4775.94476 -2681.28020 2724.57730
## 422 423 424 425 426 427
## -14054.77697 -2992.75145 -9299.26948 2737.54434 6781.79940 6494.37648
## 428 429 430 431 432 433
## -3976.33267 -4155.82526 -4797.37436 -1906.00312 -5828.35066 -6790.38990
## 434 435 436 437 438 439
## -6164.83621 -1644.46431 -1074.99429 -5175.31417 2354.80540 4680.38083
## 440 441 442 443 444 445
## -5131.11645 -2278.59955 1451.81760 -3920.40203 2722.55855 -6636.31233
## 446 447 448 449 450 451
## -12235.05133 -4759.85834 9383.00458 -2133.75665 4648.39251 -5896.64182
## 452 453 454 455 456 457
## -1211.15269 300.63472 2965.90928 -12277.13233 3217.51216 -6783.79816
## 458 459 460 461 462 463
## 6375.45750 2972.54344 2515.95156 -3801.37619 2089.85113 21.97103
## 464 465 466 467 468 469
## 1824.06988 -466.62482 3396.71887 -2552.55741 5853.58294 -6822.56758
## 470 471 472 473 474 475
## -2938.96859 -2211.52287 -4687.57985 2927.23392 7782.79747 -5926.03999
## 476 477 478 479 480 481
## 1496.42752 -6141.82727 -2880.29550 1954.63439 -12945.82689 -9921.52242
## 482 483 484 485 486 487
## -1459.10120 -207.59881 -1149.96133 -1507.36768 -9735.82277 10853.35736
## 488 489 490 491 492 493
## 6173.50083 7452.61278 -5308.86793 5417.20971 9410.25809 6277.44004
## 494 495 496 497 498 499
## -13198.99566 -10489.42299 -3498.08431 -1188.65295 -600.31576 -7688.78709
## 500 501 502 503 504 505
## 471.41261 4184.85771 5483.17883 716.38774 146.07751 -7172.49242
## 506 507 508 509 510 511
## 546.07197 -5052.18487 1775.60781 -1312.89934 -8178.75910 -712.93945
## 512 513 514 515 516 517
## -2767.92245 -692.22261 1244.36387 -9544.65147 -7917.97273 24066.28516
## 518 519 520 521 522 523
## 9949.50233 6117.77175 -5048.89603 2986.10639 17223.48257 11869.59592
## 524 525 526 527 528 529
## -23658.05210 -4932.57142 -3679.38423 4579.68493 -285.48862 -11038.43697
## 530 531 532 533 534 535
## 4315.57517 13907.32483 -4797.34030 4472.56931 5701.33146 -1588.68101
## 536 537 538 539 540 541
## -4383.13680 -6987.78039 -2106.65461 8299.50316 219.79179 -8052.27223
## 542 543 544 545 546 547
## 1799.09925 -582.85920 381.63377 -11004.54254 -11168.45957 1818.98783
## 548 549 550 551 552 553
## 6847.18817 -1355.44916 796.77961 -7735.76279 8464.23145 936.39348
## 554 555 556 557 558 559
## -11898.64787 9060.10626 8692.37544 244.41185 4986.58637 -3391.79645
## 560 561 562 563 564 565
## 14227.76089 21784.11543 -5947.01986 -9308.92030 6980.20458 493.57334
## 566 567 568 569 570 571
## 3693.76935 -7121.88654 -17172.83844 6572.49957 6450.31829 2005.75307
## 572 573 574 575 576 577
## 3220.32447 1922.96318 -2001.97513 14839.31146 -9351.33932 -6102.20954
## 578 579 580 581 582 583
## 8763.52314 3020.87137 -6361.68797 7593.35766 -3624.36743 -2663.63080
## 584 585 586 587 588 589
## 15773.04164 -14232.99680 8482.86935 232.17958 -6063.16267 -694.12333
## 590 591 592 593 594 595
## 304.17615 -10595.02390 1723.25540 -7174.52659 2962.44928 8825.14784
## 596 597 598 599 600 601
## -7414.08051 5841.44626 2810.22063 6965.06886 -2999.65178 6283.41814
## 602 603 604 605 606 607
## -8095.27447 2335.99902 1378.65699 3258.62499 1649.27879 559.88318
## 608 609 610 611 612 613
## -5654.60033 8146.70093 -1008.15135 -2426.27611 -3346.60102 -8167.88739
## 614 615 616 617 618 619
## 11918.53891 5066.25864 -9127.68103 11680.92889 6257.49261 -5304.43817
## 620 621 622 623 624 625
## 26533.19814 -12323.62442 -6496.08782 3328.89968 -3957.14368 -10457.95991
## 626 627 628 629 630 631
## 11289.39096 -21482.75643 -2550.25747 8540.27239 11144.80767 -1386.93194
## 632 633 634 635 636 637
## 33420.56676 -6023.76324 6132.02223 5845.32532 -1799.39183 -4955.57003
## 638 639 640 641 642 643
## -1659.23250 -12200.05871 -2191.39555 -1858.04981 -2507.08328 -2865.90174
## 644 645 646 647 648 649
## 1784.53418 4441.77797 17045.53383 18857.21519 1400.61921 5259.84674
## 650 651 652 653 654 655
## 11093.70200 20718.91639 1517.25483 -27368.94680 -1093.38146 -2077.67768
## 656 657 658 659
## 2040.03966 -3000.93920 -10481.09471 1662.30204
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17342.25 20137.99 24315.11 24038.17 26349.02 23729.86 24433.24 19751.50
## 10 11 12 13 14 15 16 17
## 19492.82 16883.59 17647.01 14435.49 14485.73 15138.38 16804.30 15154.68
## 18 19 20 21 22 23 24 25
## 16170.65 15555.50 22509.98 21610.54 21099.80 22955.66 22293.82 22934.58
## 26 27 28 29 30 31 32 33
## 24747.18 18785.27 20480.24 28175.96 28232.57 27911.40 25583.64 26960.74
## 34 35 36 37 38 39 40 41
## 30734.34 31076.47 32460.18 30015.77 34052.61 37202.74 34312.03 31180.54
## 42 43 44 45 46 47 48 49
## 30049.13 20798.87 28181.75 30574.21 31643.73 38358.28 37862.00 42439.95
## 50 51 52 53 54 55 56 57
## 46599.67 39429.52 34096.12 29208.75 22477.05 28653.41 25295.83 21660.33
## 58 59 60 61 62 63 64 65
## 25989.03 27225.82 27517.47 27921.85 23867.38 40158.88 41969.52 37301.38
## 66 67 68 69 70 71 72 73
## 41432.31 46259.13 56734.54 54784.20 40294.04 37844.96 40808.34 35211.47
## 74 75 76 77 78 79 80 81
## 30745.89 21617.31 24760.94 20760.27 22808.64 17796.41 19775.42 19015.93
## 82 83 84 85 86 87 88 89
## 18061.22 16165.05 17419.73 20962.59 25300.48 26272.46 26296.97 26902.56
## 90 91 92 93 94 95 96 97
## 30953.10 29804.79 30786.08 28885.50 28099.77 28461.26 28860.50 22554.01
## 98 99 100 101 102 103 104 105
## 25514.56 18670.98 17547.78 15623.91 15918.25 16583.40 20975.21 20075.28
## 106 107 108 109 110 111 112 113
## 23522.93 23316.69 24962.52 27787.30 25330.63 21838.38 22108.82 24707.27
## 114 115 116 117 118 119 120 121
## 35375.43 33600.93 35405.20 38349.34 40269.90 38000.10 32914.71 29322.23
## 122 123 124 125 126 127 128 129
## 31367.41 29678.45 30838.65 38301.06 37949.98 37028.48 33948.70 35697.71
## 130 131 132 133 134 135 136 137
## 40998.03 40448.44 31809.16 33051.10 36183.21 32658.73 31075.15 30176.69
## 138 139 140 141 142 143 144 145
## 26795.95 28184.81 27958.61 25686.73 27674.56 26324.18 20041.95 23017.70
## 146 147 148 149 150 151 152 153
## 20883.67 23806.97 24337.20 25898.84 26077.63 27701.89 28979.12 31956.46
## 154 155 156 157 158 159 160 161
## 27508.52 26784.80 24384.25 30171.79 41642.22 40002.65 37415.23 42344.74
## 162 163 164 165 166 167 168 169
## 43727.84 47081.99 42629.89 38021.92 43349.07 59356.52 61528.65 59971.42
## 170 171 172 173 174 175 176 177
## 56854.51 55282.70 57936.41 56978.15 49409.63 52182.08 55857.04 55892.58
## 178 179 180 181 182 183 184 185
## 62892.52 53566.53 50376.22 41351.07 33043.39 36488.21 46409.01 45874.86
## 186 187 188 189 190 191 192 193
## 51719.99 57361.09 67949.11 73136.45 66945.14 67135.08 74077.55 69832.17
## 194 195 196 197 198 199 200 201
## 65435.95 54874.27 49012.36 50410.90 46085.26 38395.66 44704.24 42963.69
## 202 203 204 205 206 207 208 209
## 42608.20 43077.89 49749.06 58477.55 58113.85 59807.47 61433.32 65155.94
## 210 211 212 213 214 215 216 217
## 74451.29 66677.27 55079.90 49786.19 40945.34 37958.07 41015.43 31216.08
## 218 219 220 221 222 223 224 225
## 47882.66 55071.07 55956.66 78310.43 85669.90 87636.90 95090.98 86205.52
## 226 227 228 229 230 231 232 233
## 80312.48 79901.97 76612.52 75789.18 80440.53 81780.13 76316.22 71615.73
## 234 235 236 237 238 239 240 241
## 77167.13 64056.40 56236.91 48333.12 40096.39 44212.49 46326.29 39895.42
## 242 243 244 245 246 247 248 249
## 33689.29 43785.80 38136.74 42001.26 34405.04 33134.31 36723.49 39496.31
## 250 251 252 253 254 255 256 257
## 30491.63 36322.30 40054.97 45155.80 47824.95 47326.70 57441.35 74622.11
## 258 259 260 261 262 263 264 265
## 74440.51 67872.15 69327.19 65616.32 67036.31 60906.89 50375.48 46474.69
## 266 267 268 269 270 271 272 273
## 46680.31 42857.45 51502.36 47842.84 51914.58 50065.21 54059.02 54349.68
## 274 275 276 277 278 279 280 281
## 60258.29 57935.31 67433.34 61468.00 61674.23 60053.16 65692.18 59536.04
## 282 283 284 285 286 287 288 289
## 56162.04 45903.29 44329.02 61249.63 66679.72 67081.77 64546.07 63649.93
## 290 291 292 293 294 295 296 297
## 67578.55 71418.08 52758.26 43039.29 37158.63 47293.37 50476.58 49606.55
## 298 299 300 301 302 303 304 305
## 73365.35 79203.25 79858.84 84387.89 82619.04 77739.15 81143.29 56495.96
## 306 307 308 309 310 311 312 313
## 52824.68 52510.24 46412.68 43672.58 47210.78 39897.63 38642.83 33302.68
## 314 315 316 317 318 319 320 321
## 37018.81 36214.95 39977.16 37998.02 63318.22 61198.41 62792.65 70646.20
## 322 323 324 325 326 327 328 329
## 72990.38 98011.46 96418.44 72685.50 71505.08 69892.09 61989.28 59163.52
## 330 331 332 333 334 335 336 337
## 29655.05 33275.97 33707.55 35985.56 35338.30 41001.10 42057.26 37390.72
## 338 339 340 341 342 343 344 345
## 36619.33 36743.71 32147.26 38037.85 38689.27 38943.05 39803.03 41556.69
## 346 347 348 349 350 351 352 353
## 43345.77 43101.91 36174.02 26912.25 32160.37 31041.95 30639.41 28298.91
## 354 355 356 357 358 359 360 361
## 32893.28 36579.75 40962.74 39184.45 40421.98 42521.81 49783.50 50324.14
## 362 363 364 365 366 367 368 369
## 50522.16 52940.85 50469.72 49924.29 42702.31 39949.71 36195.71 34014.51
## 370 371 372 373 374 375 376 377
## 30143.49 37299.79 39545.45 47288.59 41369.08 40826.23 39389.95 38931.16
## 378 379 380 381 382 383 384 385
## 29963.73 34478.81 27655.94 35726.88 45873.39 49374.46 47679.00 49634.73
## 386 387 388 389 390 391 392 393
## 55742.59 65056.81 58390.67 52958.74 52692.34 59938.71 60452.64 68964.84
## 394 395 396 397 398 399 400 401
## 58267.15 59805.75 59373.80 58866.98 57381.19 56165.10 43162.69 51593.40
## 402 403 404 405 406 407 408 409
## 50611.86 49596.75 55882.53 48559.57 47883.16 46240.02 41995.26 40847.06
## 410 411 412 413 414 415 416 417
## 38945.87 33146.56 40877.33 43750.88 38519.31 33696.15 48299.54 52074.98
## 418 419 420 421 422 423 424 425
## 55933.38 48538.37 44927.99 43627.85 47149.63 35777.61 35511.70 29874.03
## 426 427 428 429 430 431 432 433
## 35363.06 43540.48 50308.33 47132.11 44253.66 41234.29 41124.49 37665.82
## 434 435 436 437 438 439 440 441
## 33873.84 31157.75 32705.42 34521.46 32562.05 37340.48 43434.12 40245.03
## 442 443 444 445 446 447 448 449
## 39956.33 42908.54 40832.73 44750.31 40082.91 31276.86 30135.28 41287.47
## 450 451 452 453 454 455 456 457
## 40974.75 46524.07 42238.87 42582.22 44173.52 47824.70 37881.49 42643.37
## 458 459 460 461 462 463 464 465
## 38149.11 45581.74 49038.33 51611.66 48400.15 50698.74 50896.64 52612.20
## 466 467 468 469 470 471 472 473
## 52118.85 55009.56 52385.99 57346.14 50727.54 48381.52 46993.15 43678.34
## 474 475 476 477 478 479 480 481
## 47366.77 54695.61 49223.00 50895.54 45778.30 44186.51 46968.40 36573.38
## 482 483 484 485 486 487 488 489
## 30250.96 32086.60 34734.68 36197.80 37146.25 30901.64 43206.07 49746.24
## 490 491 492 493 494 495 496 497
## 56453.44 51260.22 56006.17 63502.27 67245.00 53748.99 44496.66 42557.22
## 498 499 500 501 502 503 504 505
## 42874.60 43651.50 38237.59 40593.29 45799.25 51378.47 52075.35 52183.92
## 506 507 508 509 510 511 512 513
## 45999.36 47315.18 43641.82 46347.61 46019.33 39848.37 40959.07 40149.08
## 514 515 516 517 518 519 520 521
## 41234.78 43827.22 36796.40 32160.86 55619.93 63633.51 67220.61 60719.04
## 522 523 524 525 526 527 528 529
## 62034.37 75375.12 82226.05 57627.86 52590.38 49344.32 53644.35 53159.58
## 530 531 532 533 534 535 536 537
## 43520.14 48421.96 60854.20 55473.86 58810.24 62726.11 59831.85 54952.21
## 538 539 540 541 542 543 544 545
## 48532.37 47212.50 55006.49 54761.42 47455.62 49639.14 49468.94 50150.26
## 546 547 548 549 550 551 552 553
## 40967.89 32950.87 37214.38 45184.59 44985.22 46660.33 40778.20 49628.61
## 554 555 556 557 558 559 560 561
## 50763.08 40726.61 50095.48 57816.45 57192.84 60725.65 56569.24 68117.60
## 562 563 564 565 566 567 568 569
## 84505.16 74774.92 63544.80 67884.28 66042.52 67207.74 58929.84 43207.79
## 570 571 572 573 574 575 576 577
## 50089.97 55888.53 57049.96 59088.04 59723.40 56901.69 68927.34 58492.50
## 578 579 580 581 582 583 584 585
## 52328.76 59793.13 61269.97 54488.64 60642.08 56298.06 53395.96 66721.14
## 586 587 588 589 590 591 592 593
## 52412.70 59624.39 58733.16 52568.69 51886.40 52157.45 43040.89 45787.24
## 594 595 596 597 598 599 600 601
## 40510.69 44679.85 53284.94 46736.55 52489.78 54824.65 60391.37 56618.87
## 602 603 604 605 606 607 608 609
## 61345.70 53066.57 54912.63 55674.95 57941.44 58505.12 58054.17 52336.73
## 610 611 612 613 614 615 616 617
## 59270.87 57365.99 54515.60 51281.17 44371.18 55673.60 59490.82 50589.93
## 618 619 620 621 622 623 624 625
## 60804.08 64913.44 58520.80 80346.91 65738.37 58206.24 60173.00 55610.25
## 626 627 628 629 630 631 632 633
## 46120.18 56634.18 37541.69 37404.44 46799.91 57093.22 55173.15 83383.19
## 634 635 636 637 638 639 640 641
## 73746.69 75907.67 77515.39 72337.00 65187.80 61882.92 50006.40 48404.19
## 642 643 644 645 646 647 648 649
## 47315.80 45825.47 44239.32 46867.79 51401.75 66102.07 80265.67 77441.01
## 650 651 652 653 654 655 656 657
## 78328.44 84093.80 97295.46 92148.80 62956.24 60454.11 57463.53 58430.37
## 658 659
## 54935.67 45521.70
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8266
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.977466 0.5228785 3.578419
## t2* 1816.543330 22.9305227 218.853517
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.340894 8.126542 14.93161
## 2 lag_depvar 1505.446676 1824.912296 2220.95426Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jan 08 01:40:44 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jan 08 01:40:51 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jan 08 01:40:58 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jan 08 01:41:05 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jan 08 01:41:12 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jan 08 01:41:19 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jan 08 01:41:26 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jan 08 01:41:33 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jan 08 01:41:40 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jan 08 01:41:47 2024
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 5.195333 | 5.410333 | 5.629750 | 6.5981458 |
| Comida | 366.009167 | 310.278417 | 314.087500 | 346.7794583 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 38.104750 | 47.072333 | 38.297667 | 33.8261667 |
| Enceres | 18.259750 | 20.086417 | 17.443792 | 23.0398333 |
| Farmacia | 4.733250 | 1.831667 | 7.913875 | 8.6494375 |
| Gas/Bencina | 35.219333 | 44.325000 | 28.954333 | 27.5965833 |
| Diosi | 55.804250 | 31.180667 | 41.934250 | 44.1985208 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 5.7233125 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 17.2805833 |
| VTR | 12.829167 | 25.156667 | 22.121792 | 19.0466250 |
| Netflix | 4.555500 | 7.151583 | 7.090167 | 6.7457708 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.7877708 |
| Total | 540.710500 | 499.588167 | 522.488250 | 540.2722083 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2191, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-12-31 00:04:58 sería de: 37.564 pesos// Percentil 95% más alto proyectado: 40.778,21
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36833.92 | 36828.51 |
| Lo.80 | 36883.04 | 36886.39 |
| Point.Forecast | 37564.03 | 39253.82 |
| Hi.80 | 39365.36 | 43947.75 |
| Hi.95 | 40353.63 | 46432.57 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,0,0) with non-zero mean
##
## Coefficients:
## mean
## 1020.0204
## s.e. 22.4275
##
## sigma^2 = 30188: log likelihood = -387.51
## AIC=779.02 AICc=779.24 BIC=783.18paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2362 835.0084 6.1375
## s.e. 0.1436 294.8750 9.4651
##
## sigma^2 = 27668: log likelihood = -377.92
## AIC=763.85 AICc=764.6 BIC=772.09forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 747.5655 | 679.4817 | 743.9676 |
| Lo.80 | 863.5164 | 797.3541 | 832.2183 |
| Point.Forecast | 1082.5528 | 1020.0204 | 1028.4857 |
| Hi.80 | 1301.5893 | 1242.6866 | 1313.6949 |
| Hi.95 | 1417.6546 | 1360.5590 | 1495.4148 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.14 scales_1.3.0 ggiraph_0.8.8
## [7] tidytext_0.4.1 DT_0.31 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.3 xts_0.13.1
## [13] forecast_8.21.1 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.3 forcats_1.0.0
## [22] dplyr_1.1.4 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
## [28] sjPlot_2.8.15 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-4 sparklyr_1.8.4 httr_1.4.7
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.1
## [37] stringi_1.8.3 data.table_1.14.10 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.9 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.5
## [16] askpass_1.1 timechange_0.2.0 anytime_0.3.9
## [19] tseries_0.10-55 colorspace_2.1-0 xfun_0.39
## [22] crayon_1.5.2 jsonlite_1.8.4 lme4_1.1-35.1
## [25] glue_1.6.2 gtable_0.3.4 emmeans_1.9.0
## [28] sjstats_0.18.2 sjmisc_2.8.9 car_3.1-2
## [31] quantmod_0.4.25 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.0 ggeffects_1.3.4 Rcpp_1.0.10
## [37] viridisLite_0.4.2 xtable_1.8-4 performance_0.10.8
## [40] bit_4.0.5 htmlwidgets_1.6.2 timeSeries_4032.108
## [43] gplots_3.1.3 ellipsis_0.3.2 spatial_7.3-14
## [46] pkgconfig_2.0.3 farver_2.1.1 nnet_7.3-16
## [49] sass_0.4.5 dbplyr_2.4.0 janitor_2.2.0
## [52] utf8_1.2.3 tidyselect_1.2.0 labeling_0.4.3
## [55] rlang_1.1.2 munsell_0.5.0 cellranger_1.1.0
## [58] tools_4.1.2 cachem_1.0.7 cli_3.6.1
## [61] generics_0.1.3 sjlabelled_1.2.0 broom_1.0.5
## [64] evaluate_0.20 fastmap_1.1.1 yaml_2.3.7
## [67] knitr_1.45 bit64_4.0.5 caTools_1.18.2
## [70] nlme_3.1-153 slam_0.1-50 xml2_1.3.3
## [73] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.14
## [76] curl_5.2.0 bslib_0.4.2 highr_0.10
## [79] fBasics_4032.96 Matrix_1.6-4 its.analysis_1.6.0
## [82] nloptr_2.0.3 urca_1.3-3 vctrs_0.6.5
## [85] pillar_1.9.0 lifecycle_1.0.3 lmtest_0.9-40
## [88] jquerylib_0.1.4 estimability_1.4.1 bitops_1.0-7
## [91] insight_0.19.7 R6_2.5.1 KernSmooth_2.23-20
## [94] janeaustenr_1.0.0 codetools_0.2-18 assertthat_0.2.1
## [97] boot_1.3-28 MASS_7.3-54 gtools_3.9.5
## [100] openssl_2.0.6 withr_2.5.2 fracdiff_1.5-2
## [103] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [106] quadprog_1.5-8 timeDate_4032.109 minqa_1.2.6
## [109] snakecase_0.11.1 rmarkdown_2.25 carData_3.0-5
## [112] TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))